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SSRN 2023年

作者： Luo, Chuanwen Hong, Yi Hou, Yunan Zhu, Yuqing Li, Deying Yu, Dongxiao Wu, Weili School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China Research Center of Applied Mathematics and Machine Intelligence Zhejiang Lab Zhejiang Hangzhou311121 China Department of Computer Science California State University Los AngelesCA90032 United States School of Information Renmin University of China Beijing100872 China School of Computer Science and Technology Institute of Intelligent Computing Shandong University Qingdao266510 China Department of Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV can not complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, this paper study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is used to collect data from a given WSN and replenished energy by Laser Beam Directors (LBDs). The problem aims at optimizing the flight trajectory of UAV for ensuring the real-time performance of data, which is to design the detailed hovering and traveling plans of UAV for collecting data from sensors and charged by LBDs such that all sensory data of WSN are collected by UAV and transported to the base *** prove that the RDCL problem is NP-hard. To solve the problem, we first study two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Afterwards, we propose an approximation algorithm to solve the RDCL problem based on TODC and CTO problems. Finally, the proposed algorithm is verified by extensive simulations. © 2023, The Authors. All rights reserved.

关键词： Wireless sensor networks

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Empirical Analysis of EIP-1559: Transaction Fees, Waiting Time, and Consensus Security

arXiv

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arXiv 2022年

作者： Liu, Yulin Lu, Yuxuan Nayak, Kartik Zhang, Fan Zhang, Luyao Zhao, Yinhong Bochsler Group Switzerland Center on Frontiers of Computing Studies Peking University China Department of Computer Science Duke University United States Department of Computer Science Yale University 51 Prospect St New HavenCT06520 United States Data Science Research Center Social Science Division Duke Kunshan University No.8 Duke Ave. Kunshan Jiangsu 215316 China Duke University United States SciEcon CIC 71-75 Shelton Street Covent Garden LondonWC2H 9JQ United Kingdom

A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs. Copyright © 2022, The Authors. All rights reserved.

关键词： Blockchain

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What is the visual Cognition Gap between Humans and Multimodal LLMs?

arXiv

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arXiv 2024年

作者： Cao, Xu Lai, Bolin Ye, Wenqian Ma, Yunsheng Heintz, George Chen, Jintai Cao, Jianguo Rehg, James M. Health Care Engineering Systems Center University of Illinois Urbana-Champaign United States College of Computing Georgia Institute of Technology United States Department of Computer Science University of Virginia United States College of Engineering Purdue University United States Department of Rehabilitation Medicine Shenzhen Children's Hospital China

Recently, Multimodal Large Language Models (MLLMs) have shown great promise in language-guided perceptual tasks such as recognition, segmentation, and object detection. However, their effectiveness in addressing visual cognition problems that require high-level reasoning is not well-established. One such challenge is abstract visual reasoning (AVR) - the cognitive ability to discern relationships among patterns in a set of images and extrapolate to predict subsequent patterns. This skill is crucial during the early neurodevelopmental stages of children. Inspired by the AVR tasks in Raven's Progressive Matrices (RPM) and Wechsler Intelligence Scale for Children (WISC), we propose a new dataset MaRs-VQA and a new benchmark VCog-Bench containing three datasets to evaluate the zero-shot AVR capability of MLLMs and compare their performance with existing human intelligent investigation. Our comparative experiments with different open-source and closed-source MLLMs on the VCog-Bench revealed a gap between MLLMs and human intelligence, highlighting the visual cognitive limitations of current MLLMs. We believe that the public release of VCog-Bench, consisting of MaRs-VQA, and the inference pipeline will drive progress toward the next generation of MLLMs with human-like visual cognition abilities. The code and datasets for our benchmark are at ***/IrohXu/*** Codes 68T01 © 2024, CC BY.

关键词： Object detection

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Computational Diffusion MRI 1

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丛书名： Mathematics and visualization

1000年

作者： Enrico Kaden Francesco Grussu Lipeng Ning Chantal M. W. Tax Jelle Veraart

ISBN: (数字)9783319738390

ISBN: (纸本)9783319738383;9783030088668

This volume presents the latest developments in the highly active and rapidly growing field of diffusion MRI. The reader will find numerous contributions covering a broad range of topics, from the mathematical foundations of the diffusion process and signal generation, to new computational methods and estimation techniques for the in-vivo recovery of microstructural and connectivity features, as well as frontline applications in neuroscience research and clinical practice.;These proceedings contain the papers presented at the 2017 MICCAI Workshop on Computational Diffusion MRI (CDMRI’17) held in Québec, Canada on September 10, 2017, sharing new perspectives on the most recent research challenges for those currently working in the field, but also offering a valuable starting point for anyone interested in learning computational techniques in diffusion MRI. This book includes rigorous mathematical derivations, a large number of rich, full-colour visualisations and clinically relevant results. As such, it will be of interest to researchers and practitioners in the fields of computer science, MRI physics and applied mathematics.

关键词： Mathematical and Computational Biology Statistics for Life sciences, Medicine, Health sciences Computational Mathematics and Numerical Analysis Computational Biology/Bioinformatics Image Processing and computer Vision

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Auxiliary-free replica shadow estimation

arXiv

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arXiv 2024年

作者： Liu, Qing Li, Zihao Yuan, Xiao Zhu, Huangjun Zhou, You Key Laboratory for Information Science of Electromagnetic Waves Ministry of Education Fudan University Shanghai200433 China State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200433 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Center on Frontiers of Computing Studies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Efficiently measuring nonlinear properties, like the entanglement spectrum, is a significant yet challenging task from quantum information processing to many-body physics. Current methodologies often suffer from an exponential scaling of the sampling cost or require auxiliary qubits and deep quantum circuits. To address these limitations, we propose an efficient auxiliary-free replica shadow (AFRS) framework, which leverages the power of the joint entangling operation on a few input replicas while integrating the mindset of shadow estimation. We rigorously prove that AFRS can offer exponential improvements in estimation accuracy compared with the conventional shadow method, and facilitate the simultaneous estimation of various nonlinear properties, unlike the destructive swap test. Additionally, we introduce an advanced local-AFRS variant tailored to estimating local observables with even constant-depth local quantum circuits, which significantly simplifies the experimental realization compared with the general swap test. Our work paves the way for the application of AFRS on near-term quantum hardware, opening new avenues for efficient and practical quantum measurements. © 2024, CC BY.

关键词： Quantum entanglement

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Scalability of high-performance PDE solvers

arXiv

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arXiv 2020年

作者： Fischer, Paul Min, Misun Rathnayake, Thilina Dutta, Som Kolev, Tzanio Dobrev, Veselin Camier, Jean-Sylvain Kronbichler, Martin Warburton, Tim Swirydowicz, Kasia Brown, Jed Mathematics and Computer Science Argonne National Laboratory LemontIL60439 Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 Institute for Computational Mechanics Technical University of Munich Garching b. Muenchen85748 Germany Department of Computer Science University of Colorado BoulderCO80309 National Renewable Energy Laboratory LakewoodCO80401 Department of Mathematics Virginia Tech BlacksburgVA24061 Mechanical & Aerospace Engineering Utah State University UT84322

Performance tests and analyses are critical to effective HPC software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this paper, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for PDEs that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite *** Codes 35-04 Copyright © 2020, The Authors. All rights reserved.

关键词： computer architecture

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Transferring knowledge distillation for multilingual social event detection

arXiv

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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Grid data management systems & services 03

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Proceedings of the 29th international conference on Very large data bases - Volume 29

作者： Arun Jagatheesan Reagan Moore Norman W. Paton Paul Watson San Diego Supercomputer Center University of California at San Diego La Jolla CA and Institute for High Energy Physics and Astrophysics University of Florida Gainesville FL San Diego Supercomputer Center University of California at San Diego La Jolla CA Department of Computer Science University of Manchester Manchester UK School of Computing Science University of Newcastle-upon-Tyne Newcastle-upon-Tyne UK

ISBN: (纸本)9780127224428

The Grid is an emerging infrastructure for providing coordinated and consistent access to distributed, heterogeneous computational and information storage resources amongst autonomous *** grids are being built across the world as the next generation data handling systems for sharing access to data and storage systems within multiple administrative domains. A data grid provides logical name spaces for digital entities and storage resources to create global identifiers that are location independent. Data grid systems provide services on the logical name space for the manipulation, management, and organization of digital *** are increasingly being used within Grid applications for data and metadata management, and several groups are now developing services for the access and integration of structured data on the Grid. The service-based approach to making data available on the Grid is being encouraged by the adoption of the Open Grid Services Architecture (OGSA), which is bringing about the integration of the Grid with Web Service *** tutorial will introduce the Grid, and examine the requirements, issues and possible solutions for integrating data into the Grid. It will take examples from current systems, in particular the SDSC Storage Resource Broker and the OGSA-Database Access and Integration project.

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Book reviews

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Minds and Machines 1993年第3期3卷 335-375页

作者： Erwin M. Segal Meredith Williams David J. Cole James Geller Yorick Wilks Shoshana Loeb Kim Sterelny Jerry Fodor Sara Heinämaa Ausonio Marras Department of Psychology and Center for Cognitive Science State University of New York at Buffalo Buffalo USA Department of Philosophy Northwestern University Evanston USA Department of Philosophy University of Minnesota Duluth USA Department of Computer and Information Science New Jersey Institute of Technology Newark USA Computing Research Laboratory New Mexico State University Las Cruces USA Bell Communications Research Morristown USA Department of Philosophy Victoria University of Wellington Wellington New Zealand Department of Philosophy Rutgers University New Brunswick USA Philosophy Program CUNY Graduate Center New York USA Department of Philosophy University of Helsinki Helsinki Finland Department of Philosophy University of Western Ontario London Canada

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SurgicalGaussian: Deformable 3D Gaussians for High-Fidelity Surgical Scene Reconstruction

arXiv

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arXiv 2024年

作者： Xie, Weixing Yao, Junfeng Cao, Xianpeng Lin, Qiqin Tang, Zerui Dong, Xiao Guo, Xiaohu Center for Digital Media Computing School of Film School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China Department of Computer Science The University of Texas at Dallas Dallas United States

Dynamic reconstruction of deformable tissues in endoscopic video is a key technology for robot-assisted surgery. Recent reconstruction methods based on neural radiance fields (NeRFs) have achieved remarkable results in the reconstruction of surgical scenes. However, based on implicit representation, NeRFs struggle to capture the intricate details of objects in the scene and cannot achieve real-time rendering. In addition, restricted single view perception and occluded instruments also propose special challenges in surgical scene reconstruction. To address these issues, we develop SurgicalGaussian, a deformable 3D Gaussian Splatting method to model dynamic surgical scenes. Our approach models the spatio-temporal features of soft tissues at each time stamp via a forward-mapping deformation MLP and regularization to constrain local 3D Gaussians to comply with consistent movement. With the depth initialization strategy and tool mask-guided training, our method can remove surgical instruments and reconstruct high-fidelity surgical scenes. Through experiments on various surgical videos, our network outperforms existing method on many aspects, including rendering quality, rendering speed and GPU usage. The project page can be found at https://***. © 2024, CC BY-SA.

关键词： Tissue

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